Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes

Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natura...

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Veröffentlicht in:	Maternal and child health journal Jg. 28; H. 3; S. 578 - 586
Hauptverfasser:	Barcelona, Veronica, Scharp, Danielle, Moen, Hans, Davoudi, Anahita, Idnay, Betina R., Cato, Kenrick, Topaz, Maxim
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2024 Springer Springer Nature B.V
Schlagworte:	Algorithms Analysis Bias Childrens health Computational linguistics Decision trees Deep learning Demographic aspects Electronic health records Gynecology Health aspects Health disparities Health problems Informatics Investment bankers Language Language processing Machine learning Maternal & child health Maternal and Child Health Maternal health services Medical records Medicine Medicine & Public Health Natural language interfaces Natural language processing Pediatrics Performance evaluation Population Economics Power Public Health Sociology Stigma Stigma (Social psychology) Support vector machines United States Electronic health records Bias Natural language processing
ISSN:	1092-7875, 1573-6628, 1573-6628
Online-Zugang:	Volltext
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